Data Scientist - Take Home Test¶

Background¶

I am an aspiring YouTuber and my dream is for one of my future videos to be trending. In order to do so, I want to get some insights from the past trending videos on YouTube. My friend already helped me to extract the data regarding trending videos, but neither of us have the skill to analyze the data.

Objectives¶

Please provide me a summary regarding the common factors of the trending videos, some things that I could think of:

specific topic/category that the video falls onto
who uploaded these videos
what are written on the title, tags, or description
the engagement metrics (views, likes, dislikes, comments)
or anything else that you can find

I understand that there might be different combinations that allow videos to be trending, so I appreciate if you could also provide me with video groups/segmentations with proper labelling (e.g. Group 1 - Superhero Trailers, Group 2 - Videos with animal tag) and relevant statistics.

Metadata¶

The following are some descriptions for the given columns in each dataset. If some columns are not mentioned, you may ignore those for further analytics.

youtube-videos.parquet¶

video_id: unique ID for a Youtube video, note that this value is NOT unique in this dataset (the same video can be listed more than once on multiple trending_date)
trending_date: when the specific video is trending (in YY.DD.MM format)
title: title of the video (what you see before clicking on a YT video)
channel_title: channel that published the video
category_id: unique ID for the video category (see column id in the other dataset)
publish_time: when the video was published (in YYYY-MM-DDThh:mm:ssZ format) tags: YouTube tags, separated by delimiter “|” views, likes, dislikes, comment_count: user engagement metrics towards the video
comments_disabled: whether comment is disabled for the video
ratings_disabled: whether rating system is disabled for the video
description: extra sentences written by the uploader to explain the video content

youtube-categories.json¶

id: unique ID for the category, refers to category_id on the other dataset
title: title of the given category
channel_id: unique identifier for the channel

Libraries¶

!pip install contractions

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!pip install nltk==3.7

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!pip install kmodes

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
from google.colab import drive

import nltk
import string
import contractions
from nltk.tokenize import word_tokenize
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud
from collections import Counter
from nltk.probability import FreqDist
from nltk.util import ngrams
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation, NMF
from scipy import stats
from sklearn.preprocessing import LabelEncoder  
from sklearn.preprocessing import StandardScaler  
from kmodes.kmodes import KModes  
from kmodes.kprototypes import KPrototypes
import pyLDAvis.sklearn

sns.set_theme(style="darkgrid", palette='flare')

/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
  from collections import Iterable
/usr/local/lib/python3.7/dist-packages/past/builtins/misc.py:4: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
  from collections import Mapping

Data Preparation¶

drive.mount('/content/gdrive')

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).

df1 = pd.read_parquet('/content/gdrive/MyDrive/youtube-videos.parquet')
df1.head()

df2 = pd.read_json("/content/gdrive/MyDrive/youtube-categories.json")
df2

df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 40949 entries, 0 to 40948
Data columns (total 16 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   video_id                40949 non-null  object
 1   trending_date           40949 non-null  object
 2   title                   40949 non-null  object
 3   channel_title           40949 non-null  object
 4   category_id             40949 non-null  int64 
 5   publish_time            40949 non-null  object
 6   tags                    40949 non-null  object
 7   views                   40949 non-null  int64 
 8   likes                   40949 non-null  int64 
 9   dislikes                40949 non-null  int64 
 10  comment_count           40949 non-null  int64 
 11  thumbnail_link          40949 non-null  object
 12  comments_disabled       40949 non-null  bool  
 13  ratings_disabled        40949 non-null  bool  
 14  video_error_or_removed  40949 non-null  bool  
 15  description             40379 non-null  object
dtypes: bool(3), int64(5), object(8)
memory usage: 4.2+ MB

df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32 entries, 0 to 31
Data columns (total 4 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   kind     32 non-null     object
 1   etag     32 non-null     object
 2   id       32 non-null     int64 
 3   snippet  32 non-null     object
dtypes: int64(1), object(3)
memory usage: 1.1+ KB

df1.drop(['thumbnail_link'], axis=1, inplace=True)

# Normalize json
snippet = df2['snippet'].apply(lambda a: pd.Series(a))

# alternatives
# snippet = pd.DataFrame(df2['snippet'].tolist())
# from pandas.io.json import json_normalize
# snippet = json_normalize(df2.snippet)

df2 = pd.concat([df2, snippet], axis=1)

# Drop unnecessary columns
df2.drop(['kind', 'etag', 'snippet', 'channelId', 'assignable'], axis=1, inplace=True)
df2.rename(columns = {'title':'category'}, inplace = True)

data = pd.merge(df1,df2,how='inner', left_on='category_id', right_on='id')
data.head()

# Convert to Datetime
data.trending_date = pd.to_datetime(data.trending_date, format='%y.%d.%m')
data.publish_time = pd.to_datetime(data.publish_time)
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 40949 entries, 0 to 40948
Data columns (total 17 columns):
 #   Column                  Non-Null Count  Dtype              
---  ------                  --------------  -----              
 0   video_id                40949 non-null  object             
 1   trending_date           40949 non-null  datetime64[ns]     
 2   title                   40949 non-null  object             
 3   channel_title           40949 non-null  object             
 4   category_id             40949 non-null  int64              
 5   publish_time            40949 non-null  datetime64[ns, UTC]
 6   tags                    40949 non-null  object             
 7   views                   40949 non-null  int64              
 8   likes                   40949 non-null  int64              
 9   dislikes                40949 non-null  int64              
 10  comment_count           40949 non-null  int64              
 11  comments_disabled       40949 non-null  bool               
 12  ratings_disabled        40949 non-null  bool               
 13  video_error_or_removed  40949 non-null  bool               
 14  description             40379 non-null  object             
 15  id                      40949 non-null  int64              
 16  category                40949 non-null  object             
dtypes: bool(3), datetime64[ns, UTC](1), datetime64[ns](1), int64(6), object(6)
memory usage: 4.8+ MB

# Data without duplicated video, keep the last record of the video
data_clean = data.drop_duplicates(subset ="video_id", keep='last')
len(data_clean)

6351

Exploratory Data Analysis¶

Which video category has the largest number of trending videos?¶

cat_cnt = data_clean.category.value_counts().to_frame().reset_index()
cat_cnt.rename(columns={'index':'Category', 'category':'Num_Videos'}, inplace = True)
plt.figure(figsize=(10,8))
sns.barplot(data = cat_cnt, y='Category', x = 'Num_Videos')
plt.xlabel('Number of Videos', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Number of Videos based on Category', fontsize=20)
plt.show()

pct_ent = round(cat_cnt[cat_cnt['Category'] == 'Entertainment'].Num_Videos/len(data_clean)*100,2)[0]
print('{}% of all videos are videos with the entertainment category'.format(pct_ent))

25.46% of all videos are videos with the entertainment category

The category that has the most trending videos is entertainment with 1617 videos, followed by music with 801 videos and style with 595 videos.
The entertainment category has twice as many trending videos as the music category.
The categories that have the least trending videos are shows and Nonprofits/Activism.
25.46% of all videos are videos with the entertainment category

Which channels produced most trending videos?¶

chn_cnt = data_clean.channel_title.value_counts().to_frame().reset_index().head(10)
chn_cnt.rename(columns={'index':'Channel', 'channel_title':'Num_Videos'}, inplace = True)
plt.figure(figsize=(10,8))
sns.barplot(data = chn_cnt, y='Channel', x = 'Num_Videos')

plt.xlabel('Number of Videos', fontsize=20)
plt.ylabel('Channel', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Top 10 Channel with Most Trending Videos', fontsize=20)
plt.show()

based on the top 10 trending videos, there are 6 out of 10 channels in the entertainment category, such as TheEllenhow, The Tonight Show Starring Jimmy Fallon, Jimmi Kimmel Live, etc.
Channel with the Most Trending Videos is ESPN (Sports) with 84 Videos, followed by TheEllenhow, and The Tonight Show Starring Jimmy Fallon

data_clean.columns

Index(['video_id', 'trending_date', 'title', 'channel_title', 'category_id',
       'publish_time', 'tags', 'views', 'likes', 'dislikes', 'comment_count',
       'comments_disabled', 'ratings_disabled', 'video_error_or_removed',
       'description', 'id', 'category'],
      dtype='object')

com_dis = data_clean.comments_disabled.value_counts().to_frame().reset_index().head(10)
com_dis.rename(columns={'index':'boole', 'channel_title':'comments_disabled'}, inplace = True)
com_dis.boole = com_dis.boole.astype('str')
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16, 4))

sns.countplot(x='comments_disabled', data=data_clean, ax=ax1)
# plt.ylabel('Freq', fontsize=15)
# plt.yticks(fontsize=15)
# plt.xticks(fontsize=15)
# plt.title('Comments Disabled', fontsize=15)


sns.countplot(x='ratings_disabled', data=data_clean, ax=ax2)

# plt.ylabel('Freq', fontsize=15)
# plt.yticks(fontsize=15)
# plt.xticks(fontsize=15)
# plt.title('Ratings Disabled', fontsize=15)

sns.countplot(x='video_error_or_removed', data=data_clean, ax=ax3)

# plt.ylabel('Freq', fontsize=15)
# plt.yticks(fontsize=15)
# plt.xticks(fontsize=15)
# plt.title('Video Error or Removed', fontsize=15)

<matplotlib.axes._subplots.AxesSubplot at 0x7f84bca8d990>

Video Titles¶

# Just take data type object, and 
data_str = data.select_dtypes('object')
data_str.drop_duplicates(subset ="video_id", keep='first', inplace=True)
data_str

/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return func(*args, **kwargs)

## Data Processing

df_title = pd.DataFrame()

# Expanding Contractions
df_title['title_cleaned'] = data_str['title'].apply(lambda x: [contractions.fix(word) for word in x.split()])
df_title['title_cleaned'] = [' '.join(map(str, l)) for l in df_title['title_cleaned']]

# Converting all Characters to Lowercase
df_title['title_cleaned'] = df_title['title_cleaned'].str.lower()

# Remove punctuation
df_title['title_cleaned'] = df_title['title_cleaned'].str.replace(r'[^\w\s]+', '')

# Remove stopwords
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
df_title['title_cleaned'] = df_title['title_cleaned'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))

# Tokenization
nltk.download('punkt')
df_title["title_tokenized"] = df_title["title_cleaned"].apply(word_tokenize)

df_title.head()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: The default value of regex will change from True to False in a future version.
  del sys.path[0]
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!

# collect data into one list
title_words = []
for wordlist in df_title['title_tokenized']:
    title_words += wordlist
print(title_words)

['want', 'talk', 'marriage', 'meo', 'cats', 'commercial', 'affairs', 'ex', 'boyfriends', '18million', 'net', 'worth', 'google', 'us', 'shawn', 'andrew', 'blindfolded', 'cake', 'decorating', 'contest', 'molly', 'burke', 'grace', 'helbig', 'wearing', 'online', 'dollar', 'store', 'makeup', 'week', 'mark', 'ruffalo', 'reads', 'hilarious', 'thirst', 'tweets', 'idiots', 'guide', 'japanese', 'squat', 'toilets', 'russian', 'bots', 'trolls', 'november', '8', '2017', 'act', '2', 'full', 'frontal', 'tbs', 'ultimate', 'red', 'lip', 'look', 'nicole', 'guerriero', 'could', 'anastasia', 'become', 'disney', 'princess', 'mdna', 'skin', 'express', 'picked', 'girlfriends', 'outfit', 'blindfolded', 'love', 'price', 'right', 'wooo', 'kevin', 'see', 'around', 'official', 'audio', 'boyfriend', 'makeup', 'madelaine', 'petsch', 'apple', 'clips', 'sample', 'kids', 'describe', 'america', 'koji', 'illustrator', 'huge', 'beauty', 'favourites', 'autumn', 'loves', 'bill', 'gates', 'bought', 'land', 'arizona', 'build', 'tech', 'city', 'baby', 'loves', 'jeopardy', 'flipping', 'supercar', 'gone', 'wrong', 'granulated', 'sugar', 'honey', 'better', 'roy', 'moore', 'november', '15', '2017', 'act', '1', 'full', 'frontal', 'tbs', 'tried', 'period', 'yoga', 'pants', 'every', 'snack', 'packing', 'victorias', 'secret', 'fashion', 'show', 'karlie', 'kloss', 'taxes', 'complicated', 'musical', 'transparent', 'yas', 'queen', 'eminem', 'speaks', 'working', 'beyonce', 'mtv', 'emas', 'fashion', 'review', 'wkristen', 'mcatee', 'grace', 'helbig', 'ranz', 'niana', 'goes', 'la', 'carpool', 'around', 'quad9', 'install', 'windows', 'dressed', 'like', '1977', 'american', 'influencer', 'awards', '2017', 'greatest', 'best', 'man', 'speech', '10', 'year', 'old', 'son', 'take', 'kids', 'restaurant', 'wild', 'turkey', 'bourbon', 'matthew', 'mcconaughey', 'give', 'back', 'thanksgiving', 'last', 'workout', 'victorias', 'secret', 'fashion', 'show', 'karlie', 'kloss', 'two', 'love', 'stories', 'teaching', 'casey', 'vlog', 'make', 'pumpkin', 'pie', 'mistakes', 'amas', 'fashion', 'review', 'w', 'kingsley', 'grace', 'helbig', 'voices', 'exposing', 'internet', 'search', 'history', 'birth', 'father', 'russia', 'know', 'existed', 'bond', 'whisper', 'challenge', 'w', 'mom', 'grace', 'helbig', 'guessing', 'objects', 'got', 'mail', '4', 'highlights', 'ea', 'predatory', 'behavior', 'announcement', 'youtube', 'back', 'echo', 'show', 'drake', 'virginia', 'black', 'commercial', 'dipclip', 'chicken', 'nugget', 'rally', 'testing', 'secure', 'sauce', 'schizophrenia', 'like', 'make', 'gift', 'box', 'paper', 'easy', 'scene', 'suicide', 'club', '2avi', 'pregnancy', 'announcement', 'two', 'miscarriages', 'baby', 'heart', 'beat', 'mall', 'santa', 'unmasked', 'liam', 'gallagher', 'come', 'back', 'official', 'video', 'let', 'us', 'talk', 'money', 'youtuber', 'quiz', 'truth', 'dare', 'w', 'merrell', 'twins', 'let', 'us', 'talk', 'money', 'everyday', 'smokey', 'eye', 'tutorial', 'cruelty', 'free', 'lucy', 'moon', 'never', 'ever', 'kylie', 'jordyn', 'victoria', 'made', 'jewelry', 'line', 'grace', 'helbig', '5', 'desserts', 'share', 'bff', 'max', 'brenner', 'chocolate', 'bar', 'boyfriend', 'tag', 'w', 'travelaine', 'madelaine', 'petsch', 'behind', 'scenes', 'victorias', 'secret', 'fashion', 'show', 'vlog', 'karlie', 'kloss', 'camera', 'future', 'diy', 'lights', 'paper', 'cut', 'light', 'box', 'art', 'best', 'art', 'ever', 'made', '23', 'feed', 'homeless', 'one', 'list', 'one', 'life', 'michelangelo', 'microsoft', 'excel', 'americans', 'try', 'explain', 'royal', 'family', 'kids', 'describe', 'perfect', 'world', 'koji', 'illustrator', 'try', 'tiny', 'cooking', '30', '30', 'list', 'lucy', 'moon', 'teaching', 'karlie', 'kloss', 'read', 'braille', 'survivalist', 'teaches', 'live', 'grid', 'christmas', 'cupcake', 'tower', 'gone', 'wrong', 'joey', 'graceffa', 'quartz', 'worlds', 'first', 'selfcleaning', 'water', 'bottle', 'skelly', 'skeleton', 'robot', 'wishing', 'merry', 'christmas', 'happy', 'new', 'year', 'lil', 'uzi', 'vert', 'way', 'life', 'goes', 'remix', 'feat', 'nicki', 'minaj', 'official', 'music', 'video', 'got', 'lips', 'permanently', 'tattooed', 'never', 'ever', 'w', 'tyler', 'oakley', 'grace', 'helbig', 'uncomfortable', 'mens', 'fashion', 'tips', 'winter', '2017', 'style', 'guide', 'doctor', 'mike', 'zendaya', 'stops', 'interview', 'fix', 'reporters', 'hair', 'dad', 'hijacks', 'elf', 'shelf', 'greatest', 'tax', 'bill', 'ever', 'sold', 'december', '6', '2017', 'act', '1', 'full', 'frontal', 'tbs', 'surprising', 'facts', 'period', 'finally', 'start', 'building', 'dream', 'home', 'vlogmas', 'day', '6', 'must', 'watch', 'viewer', 'calls', 'news', 'anchor', 'nword', 'live', 'news', 'anchor', 'responds', 'priceless', 'super', 'mario', 'cereal', 'ok', 'feel', 'sad', 'christmas', 'vlogmas', 'day', '7', 'talking', 'fans', 'undercover', 'week', '50', 'facts', 'riverdale', 'set', 'madelaine', 'petsch', 'movies', 'got', 'girlfriend', 'christmas', 'cheerleader', 'invisible', 'box', 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'adventure', 'dynamic', 'weather', 'gameplay', 'valley', 'gods', 'announcement', 'trailer', 'youtube', 'rewind', '2017', 'behind', 'scenes', 'game', 'awards', 'full', 'show', 'death', 'stranding', 'zelda', 'legend', 'zelda', 'breath', 'wild', 'expansion', 'pass', 'dlc', 'pack', '2', 'champions', 'ballad', 'trailer', 'playerunknowns', 'battlegrounds', 'game', 'awards', '2017', 'gameplay', 'trailer', 'secret', 'fan', 'fictions', 'truth', 'bombs', '2', 'legend', 'zelda', 'breath', 'wild', 'expansion', 'pass', 'champions', 'ballad', 'trailer', 'music', 'splatoon', '2', 'bts', 'nintendo', 'switch', 'playerunknowns', 'battlegrounds', 'xbox', 'action', 'trailer', 'developer', 'update', 'happy', 'new', 'year', 'overwatch', 'nintendo', 'switch', 'fastestselling', 'home', 'console', 'us', 'history', 'ign', 'news', 'matt', 'radar', 'technician', 'star', 'wars', 'battlefront', '2', 'new', 'battlefront', '2', 'mod', 'gameplay', 'youtube', 'sent', 'mystery', 'gift', 'nintendo', 'direct', 'mini', '1112018', 'dunkeys', 'best', '2017', 'aliens', 'rideplanet', 'coaster', 'solve', 'mysteries', 'detective', 'pikachu', 'big', 'sister', 'reflexes', 'fair', 'maiden', 'epic', 'npc', 'man', 'vldl', 'first', 'look', 'nintendo', 'labo', 'look', 'turned', 'ft', 'james', 'charles', 'nintendo', 'labo', 'adults', 'okay', 'beat', 'saber', 'gameplay', 'teaser', 'god', 'war', 'story', 'trailer', 'ps4', 'dragon', 'ball', 'fighterz', 'video', 'review', 'melix', 'plays', 'jumpstart', 'first', 'grade', '90s', 'games', 'fifa', '18', 'fut', 'champions', 'cup', 'barcelona', 'day', '1', 'swiss', 'group', 'round', '3', 'download', 'january', '2018', 'american', 'vs', 'brit', 'sega', 'game', 'gear', 'commercial', 'creamed', 'spinach', 'retro', 'video', 'game', 'commercial', 'ad', '42', 'history', 'super', 'mario', 'bros', 'infamous', 'level', 'camera', 'secrets', 'grand', 'theft', 'auto', 'v', 'boundary', 'break', 'amazing', 'creatures', 'discovered', 'across', 'planet', 'getting', 'spoilers', 'legend', 'zelda', 'breath', 'wild', 'dlc', 'dev', 'talk', 'ft', 'mr', 'aonuma', 'mr', 'fujibayashi', 'nintendo', 'labo', 'handson', 'kaz', 'hirai', 'stepping', 'sony', 'ceo', 'ign', 'news', 'developer', 'update', 'happy', 'year', 'dog', 'overwatch', 'best', 'pirate', 'ever', 'seen', 'altos', 'odyssey', 'trailer', 'launching', 'february', '22nd', 'preorder', '2018', 'bugatti', 'chiron', 'forza', 'motorsport', '7', 'lotto', 'powerball', 'imagine', 'armoured', 'truck', 'got', 'catch', 'nintendo', 'switch', '1', 'year', 'anniversary', 'dev', 'talk', 'ft', 'mr', 'takahashi', 'satellites', 'capture', '400', 'megapixel', 'images', 'earths', 'globe', 'himawari', '8', 'goes16', 'nintendo', 'direct', '382018', 'official', 'call', 'duty', 'black', 'ops', '4', 'teaser', 'super', 'smash', 'bros', 'switch', 'hiding', 'flames', 'realtime', 'character', 'analysis', 'super', 'smash', 'bros', 'trailer', 'analysis', 'sonic', 'mania', 'adventures', 'sneak', 'peek', 'irelia', 'blade', 'dancer', 'champion', 'trailer', 'league', 'legends', 'nintendo', 'switch', 'nindies', 'showcase', 'spring', '2018', 'surprising', 'new', 'puppy', '8', 'weeks', 'old', 'chicas', 'birthday', 'meet', 'dog', 'animated', 'leeroy', 'jenkins', 'animated', 'overwatch', 'seasonal', 'event', 'overwatch', 'archives', 'game', 'theory', 'fortnite', 'meteor', 'destroy', 'everything', 'fortnite', 'battle', 'royale', 'markiplier', 'animated', 'ghost', 'hunter', 'guys', 'get', 'finally', 'meet', 'special', 'announcement', 'transforming', 'homeless', 'womans', 'life', 'extremely', 'emotional', 'boogiedown', 'contest', 'winners', 'announced', 'clash', 'royale', 'clan', 'wars', 'season', '4', 'announce', 'trailer', 'ghost', 'pepper', 'day', '2', 'nintendo', 'labo', 'introducing', 'players', 'super', 'smash', 'bros', 'invitational', '2018', 'clash', 'royale', 'meet', 'rascals', 'new', 'card', 'playing', 'deadpool', 'ryan', 'reynolds', 'official', 'call', 'duty', 'black', 'ops', '4', 'multiplayer', 'reveal', 'trailer', 'lie', 'detector', 'test', 'girlfriend', 'game', 'theory', 'rich', 'pokemon', 'master', 'pranked', 'bought', 'dream', 'car', 'emotional', 'surprise', 'sims', '4', 'seasons', 'official', 'reveal', 'trailer', 'history', 'fortnite', 'battle', 'royale', 'know', 'gaming', 'feat', 'remix', 'pokémon', 'let', 'us', 'go', 'pikachu', 'pokémon', 'let', 'us', 'go', 'eevee', 'trailer', 'try', 'slip', 'n', 'slide', 'wrong', 'mystery', 'box', 'game', 'theory', 'fnaf', 'stumped', 'fnaf', '6', 'ultimate', 'custom', 'night', 'anthem', 'official', 'cinematic', 'trailer', '2018', 'battlefield', '5', 'official', 'multiplayer', 'trailer', 'madden', 'nfl', '19', 'official', 'reveal', 'trailer', 'game', 'theory', 'fnaf', 'theory', 'changed', 'everything', 'fnaf', '6', 'ultimate', 'custom', 'night', 'youtube', 'live', 'e3', '2018', 'monday', 'ninja', 'marshmello', 'playstation', 'ubisoft', 'todd', 'howard', 'town', 'hall', '12', 'update', 'clash', 'clans', 'official', 'elder', 'scrolls', 'vi', 'official', 'e3', 'announcement', 'teaser', 'cyberpunk', '2077', 'official', 'e3', '2018', 'trailer', 'kingdom', 'hearts', 'iii', 'e3', '2018', 'frozen', 'trailer', 'cuphead', 'dlc', 'announcement', 'trailer', 'xbox', 'one', 'windows', '10', 'steam', 'gog', 'fallout', '76', 'trailer', 'e3', '2018', 'halo', 'infinite', 'e3', '2018', 'announcement', 'trailer', 'nintendo', 'e3', '2018', 'day', '1', 'super', 'smash', 'bros', 'ultimate', 'everyone', 'trailer', 'e3', '2018', 'assassins', 'creed', 'odyssey', 'e3', '2018', 'official', 'world', 'premiere', 'trailer', 'ubisoft', 'na', 'nintendo', 'e3', '2018', 'day', '2', 'fortnite', 'celebrity', 'proam', 'fortniteproam', 'tell', 'hollywood', 'stand', 'immigrants', 'nsc', 'face', 'face', 'prescription', 'opioid', 'crisis', 'meet', 'teen', 'vogues', '2017', '21', '21', 'teen', 'vogue', 'living', 'bridge', 'stop', 'homeless', 'woman', 'staying', 'positive', 'nina', 'dobrev', 'wants', 'save', 'sharks', '30s', 'homeless', 'little', 'boy', 'gets', 'bed', 'christmas', 'obama', 'presidential', 'center', 'suicide', 'tomorrow', 'rose', 'mcgowan', 'shares', 'thoughts', 'times', 'movement', 'view', 'unrigged', 'live', 'jennifer', 'lawrence', 'tig', 'notaro', 'richard', 'painter', 'saving', 'lives', 'lead', 'overpopulation', 'яхты', 'олигархи', 'девочки', 'охотница', 'на', 'мужчин', 'разоблачает', 'взяточника', '2018', 'writers', 'guild', 'awards', 'amber', 'ruffin', 'opening', 'monologue', '5', 'books', 'worth', 'reading', 'summer', 'crazy', 'apple', 'slowing', 'older', 'iphones', 'apple', 'byte', 'apple', 'homepod', 'everything', 'know', 'buy', 'apple', 'byte', 'game', 'zones', 'game', 'zones', 's5e1', 'golden', 'summer', 'game', 'zones', 's5e5', 'isle', 'van', 'gundy']

# Word Cloud in titles
mostcommon = FreqDist(title_words).most_common(100)
wordcloud = WordCloud(width=800, height=800, background_color='white').generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 100 Most Common Words', fontsize=30)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(title_words).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Words', fontsize=20)
plt.show()

Based on chart and word cloud we can see that the word that appears the most is "official" followed by "2018" and "video". this is because 65% of videos uploaded in 2018

# Bigram Data
tokenizer = RegexpTokenizer(r"[\w']+")
ng2 = [ngrams(tokenizer.tokenize(t.lower()), n=2) for t in df_title['title_cleaned']]

ng2 = [x for y in ng2 for x in y]
ng2 = [x + '-' + y for x,y in ng2]

mostcommon = FreqDist(ng2).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(ng2).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Bigrams', fontsize=20)
plt.ylabel('Frequency of Bigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Bigrams', fontsize=20)
plt.show()

tokenizer = RegexpTokenizer(r"[\w']+")
ng3 = [ngrams(tokenizer.tokenize(t.lower()), n=3) for t in df_title['title_cleaned']]
ng3 = [x for y in ng3 for x in y]
ng3 = [x + '-' + y + '-' + z for x,y,z in ng3]

mostcommon = FreqDist(ng3).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(ng3).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,5))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Trigrams', fontsize=20)
plt.ylabel('Frequency of Trigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Trigrams', fontsize=20)
plt.show()

Based on Bigrams and Trigrams, we can conclude that the most combinations of words focus on film trailers and music videos, for example, the films Star Wars: The Last Jedi and Avengers: Infinity Wars.

Video Description¶

df_desc = pd.DataFrame()

# Expanding Contractions
df_desc['desc_cleaned'] = data_str['description'].dropna().apply(lambda x: [contractions.fix(word) for word in x.split()])
df_desc['desc_cleaned'] = [' '.join(map(str, l)) for l in df_desc['desc_cleaned']]

# Converting all Characters to Lowercase
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.lower()

# replace . and / from the link in the description
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.replace('.', ' ')
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.replace('/', ' ')

# Remove punctuation
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.replace(r'[^\w\s]+', '')

# Remove stopwords
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))

# Tokenization
df_desc["desc_tokenized"] = df_desc["desc_cleaned"].apply(word_tokenize)

df_desc.head()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  # This is added back by InteractiveShellApp.init_path()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: FutureWarning: The default value of regex will change from True to False in a future version.
  from ipykernel import kernelapp as app

desc_words = []
for wordlist in df_desc['desc_tokenized']:
    desc_words += wordlist
# print(desc_words)

# Word Cloud in titles
mostcommon = FreqDist(desc_words).most_common(100)
wordcloud = WordCloud(width=800, height=800, background_color='white').generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 100 Most Common Words', fontsize=40)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(desc_words).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Words', fontsize=20)
plt.show()

tokenizer = RegexpTokenizer(r"[\w']+")
ng2 = [ngrams(tokenizer.tokenize(t.lower()), n=2) for t in df_desc['desc_cleaned']]

ng2 = [x for y in ng2 for x in y]
ng2 = [x + '-' + y for x,y in ng2]

mostcommon = FreqDist(ng2).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(ng2).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Bigrams', fontsize=20)
plt.ylabel('Frequency of Bigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Bigrams', fontsize=20)
plt.show()

tokenizer = RegexpTokenizer(r"[\w']+")
ng3 = [ngrams(tokenizer.tokenize(t.lower()), n=3) for t in df_desc['desc_cleaned']]
ng3 = [x for y in ng3 for x in y]
ng3 = [x + '-' + y + '-' + z for x,y,z in ng3]

mostcommon = FreqDist(ng3).most_common(50)
wordcloud = WordCloud(width=1600, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(20,8), facecolor='white')
plt.imshow(wordcloud, interpolation='lanczos')
plt.axis('off')
plt.title('Top 50 Most Common Trigrams', fontsize=50)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(ng3).most_common(25)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(20,10))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 25 Most Common Trigrams', fontsize=20)
plt.show()

In the video description, most users include their social media links such as Google, Facebook, Instagram, and Twitter or other things that require links, for example download links and product links. Another word that is pretty much listed in the description is a request to subscribe to the channel

Video Tags¶

df_tags = pd.DataFrame()

# Expanding Contractions
df_tags['tags_cleaned'] = data_str['tags'].dropna().apply(lambda x: [contractions.fix(word) for word in x.split()])
df_tags['tags_cleaned'] = [' '.join(map(str, l)) for l in df_tags['tags_cleaned']]

# Converting all Characters to Lowercase
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].str.lower()

# Remove punctuation
# df_tags['desc_cleaned'] = df_tags['desc_cleaned'].str.replace('.', ' ')
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].str.replace('|', ' ')
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].str.replace(r'[^\w\s]+', '')

# Remove stopwords
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))

# Tokenization
df_tags["tags_tokenized"] = df_tags["tags_cleaned"].apply(word_tokenize)

df_tags.head()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  if sys.path[0] == '':
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: The default value of regex will change from True to False in a future version.
  del sys.path[0]

tags_words = []
for wordlist in df_tags['tags_tokenized']:
    tags_words += wordlist
# print(tags_words)

# Word Cloud in titles
mostcommon = FreqDist(tags_words).most_common(100)
wordcloud = WordCloud(width=800, height=800, background_color='white').generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 100 Most Common Tags', fontsize=40)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(tags_words).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Tags', fontsize=20)
plt.ylabel('Frequency of Tags', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Tags', fontsize=20)
plt.show()

tokenizer = RegexpTokenizer(r"[\w']+")
ng2 = [ngrams(tokenizer.tokenize(t.lower()), n=2) for t in df_tags['tags_cleaned']]

ng2 = [x for y in ng2 for x in y]
ng2 = [x + '-' + y for x,y in ng2]

mostcommon = FreqDist(ng2).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(ng2).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Bigrams', fontsize=20)
plt.ylabel('Frequency of Bigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 25 Most Common Bigrams', fontsize=20)
plt.show()

tokenizer = RegexpTokenizer(r"[\w']+")
ng3 = [ngrams(tokenizer.tokenize(t.lower()), n=3) for t in df_tags['tags_cleaned']]
ng3 = [x for y in ng3 for x in y]
ng3 = [x + '-' + y + '-' + z for x,y,z in ng3]

mostcommon = FreqDist(ng3).most_common(50)
wordcloud = WordCloud(width=1600, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(20,8), facecolor='white')
plt.imshow(wordcloud, interpolation='lanczos')
plt.axis('off')
plt.title('Top 50 Most Common Trigrams', fontsize=50)
plt.tight_layout(pad=0)
plt.show()

mostcommon_small = FreqDist(ng3).most_common(25)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(20,10))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 25 Most Common Trigrams', fontsize=20)
plt.show()

Video Comments¶

data_clean['comment_count'].describe()

count    6.351000e+03
mean     6.450380e+03
std      3.382373e+04
min      0.000000e+00
25%      3.720000e+02
50%      1.266000e+03
75%      4.061500e+03
max      1.361580e+06
Name: comment_count, dtype: float64

Video Comments Distribution¶

ax = data_clean['comment_count'].plot(kind='hist', bins=np.arange(0, 1.5e6, 1e5), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}K'.format(x/1e3))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Comments', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Comments Count Distribution', fontsize=20)
plt.show()

Comments Distribution by Category¶

data_clean['likes_log'] = np.log(data_clean['likes'] + 1)
data_clean['views_log'] = np.log(data_clean['views'] + 1)
data_clean['dislikes_log'] = np.log(data_clean['dislikes'] + 1)
data_clean['comment_log'] = np.log(data_clean['comment_count'])

plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="comment_log", data=data_clean)

plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Comments Distribution by Category', fontsize=20)
plt.show()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
/usr/local/lib/python3.7/dist-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  after removing the cwd from sys.path.

plt.figure(figsize=(10,8))
ax = sns.barplot(data = data_clean.groupby('category').agg({'comment_count':'sum'}).reset_index(), y='category', x='comment_count')

plt.xlabel('Comment Count', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Number of Comment Count by Category', fontsize=20)
plt.show()

data_clean[data_clean['comment_count'] < 100000].shape[0] / data_clean.shape[0] * 100

99.2757046134467

data_clean['comment_count'].quantile(q=0.9)

11775.0

From the distribution we can see that the majority of trending videos have less than 100k comments. 99.27% of trending videos have less than 100k comments and 90% of trending videos have less than 11,775 comments.
We can notice that only two videos that have more than 1 million comments, BTS 'Fake Love' Music Video and So Sorry from Logan Paul.
Gaming is the category with the highest average number of comments followed by Music, Comedy, and Shows.

Video Likes¶

data_clean['likes'].describe()

count    6.351000e+03
mean     5.557323e+04
std      1.938312e+05
min      0.000000e+00
25%      2.771000e+03
50%      1.190600e+04
75%      3.842850e+04
max      5.613827e+06
Name: likes, dtype: float64

ax = data_clean['likes'].plot(kind='hist', bins=np.arange(0, 6e6, 1e5), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Likes', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Likes Distribution', fontsize=20)
plt.show()

Likes Distribution by Category¶

plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="likes_log", data=data_clean)

plt.xlabel('Likes (log)', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Likes Distribution by Category', fontsize=20)
plt.show()

data_clean[data_clean['likes'] < 100000].shape[0] / data_clean.shape[0] * 100

88.33254605573926

data_clean['likes'].quantile(q=0.9)

117929.0

98.33% of trending videos have less than 500k likes
Videos with largest number of likes is BTS 'Fake Love' Music Video followed by Childish Gambino 'This Is America' Music Video.
Music and Gaming is the category with the highest average number of likes

Video Dislikes¶

data_clean['dislikes'].describe()

count    6.351000e+03
mean     3.067025e+03
std      3.198850e+04
min      0.000000e+00
25%      1.270000e+02
50%      4.440000e+02
75%      1.507000e+03
max      1.674420e+06
Name: dislikes, dtype: float64

ax = data_clean['dislikes'].plot(kind='hist', bins=np.arange(0, 1.8e6, 1e5), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}K'.format(x/1e3))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Likes', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Dislikes Distribution', fontsize=20)
plt.show()

Dislikes Distribution by Category¶

plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="dislikes_log", data=data_clean)

plt.xlabel('Dislikes (log)', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Dislikes Distribution by Category', fontsize=20)
plt.show()

data_clean[data_clean['dislikes'] < 10000].shape[0] / data_clean.shape[0] * 100

95.6857187844434

data_clean['dislikes'].quantile(q=0.9)

4514.0

95.68% of trending videos have less than 10k dislikes
Videos with largest number of likes is So Sorry from Logan Paul followed by Youtube Rewind 2017.

Video Views¶

data_clean['views'].describe()

count    6.351000e+03
mean     1.962023e+06
std      7.060074e+06
min      5.590000e+02
25%      1.589615e+05
50%      5.181070e+05
75%      1.473582e+06
max      2.252119e+08
Name: views, dtype: float64

ax = data_clean['views'].plot(kind='hist', bins=np.arange(0, 2.3e8, 1e7), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Views Distribution', fontsize=20)
plt.show()

Views Distribution by Category¶

plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="views_log", data=data_clean)

plt.xlabel('Views (log)', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Views Distribution by Category', fontsize=20)
plt.show()

data_clean[data_clean['views'] < 518107].shape[0] / data_clean.shape[0] * 100

49.9921272240592

data_clean['views'].quantile(q=0.80)

1888088.0

92.89% of trending videos have less than 5 million views
Videos with largest number of views is Childish Gambino 'This Is America' Music Video, with 225 Million Views
Gaming is the category with the highest average number of likes, followed by Music

Relationship¶

data_clean.columns

Index(['video_id', 'trending_date', 'title', 'channel_title', 'category_id',
       'publish_time', 'tags', 'views', 'likes', 'dislikes', 'comment_count',
       'comments_disabled', 'ratings_disabled', 'video_error_or_removed',
       'description', 'id', 'category', 'likes_log', 'views_log',
       'dislikes_log', 'comment_log'],
      dtype='object')

plt.figure(figsize=(10,10))

sns.heatmap(data_clean[['views', 'likes', 'dislikes', 'comment_count']].corr(), annot = True, annot_kws={'fontsize':20}, cbar_kws={"shrink": 0.9})
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Heatmap Correlation', fontsize=20)
plt.show()

plt.figure(figsize=(14,8))
ax = sns.regplot(data = data_clean, x='views', y='likes', scatter_kws={'alpha':0.5})
# plt.figure(figsize=(14,8))
# plt.plot(data_clean['views'], data_clean['likes'], 'o',markersize=10, alpha=0.2)
# plt.axis([0,0.2e8,0,0.6e6])
# plt.xlabel('Num of Views')
# plt.ylabel('Num of Likes')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.2f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(formatter))

plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Number of Likes', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Views Distribution', fontsize=20)
plt.show()

plt.figure(figsize=(14,8))
ax = sns.regplot(data = data_clean, x='views', y='likes', scatter_kws={'alpha':0.5})
# plt.figure(figsize=(14,8))
# plt.plot(data_clean['views'], data_clean['likes'], 'o',markersize=10, alpha=0.2)
plt.axis([0,0.2e8,0,0.6e6])
# plt.xlabel('Num of Views')
# plt.ylabel('Num of Likes')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.2f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(formatter))

plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Number of Likes', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Views Distribution', fontsize=20)
plt.show()

based on the heatmap, all correlations from numerical data are positive, Likes and views have the largest correlation value of 0.84.

Datetime Data¶

Number of days between trending and upload¶

# Drop duplicated data and keep first occurence
data_first = data.drop_duplicates(subset ="video_id", keep='first')
data_first['upload_to_trending'] = (data_first['trending_date'].dt.date - data_first['publish_time'].dt.date).dt.days
data_first

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until

plt.figure(figsize=(10,8))
data_first['upload_to_trending'].plot(kind='hist', bins=np.arange(0, 4500, 250), logy=True)
plt.xlabel('Number of days between trending and upload', fontsize=20)
plt.ylabel('Freq', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Distribution of Number of days between trending and upload', fontsize=20)
plt.show()

data_first['upload_to_trending'].describe().to_frame()

data_first[data_first['upload_to_trending'] < 3].shape[0] / data_first.shape[0] * 100

73.51598173515981

data_first[data_first['upload_to_trending'] == 0].shape[0] / data_first.shape[0] * 100

1.8894662257912138

data_first[data_first['upload_to_trending'] > 3000].shape[0] / data_first.shape[0] * 100

0.1259644150527476

data_first.sort_values('upload_to_trending', ascending=False).head()

73.51% of videos become trending in less than 3 days
only 1.88% of videos are trending on the same day as when they were uploaded
largest Number of days between trending and upload is 4125 days or about 11 years, the video is titled 'Budweiser - Original Whazzup? ad' uploaded by dannotv

How long those videos trending¶

trending_days = data['video_id'].value_counts().to_frame().reset_index()
trending_days.rename(columns={"index":'video_id','video_id':'trending_days'}, inplace=True)

data_merge = pd.merge(data_first, trending_days, on='video_id')

data_merge['trending_days'].describe().to_frame()

plt.figure(figsize=(8,6))
data_merge['trending_days'].plot(kind='hist')
plt.xlabel('Total of Trending Days', fontsize=20)
plt.ylabel('Freq', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Distribution of Total Trending Days', fontsize=20)
plt.show()

Consecutive Trending Days¶

data_sorted = data[['video_id', 'trending_date']].sort_values(['video_id', 'trending_date'])
s = data_sorted.groupby('video_id').trending_date.diff().dt.days.ne(1).cumsum()
s
data_sorted = data_sorted.groupby(['video_id', s]).size().reset_index(level=1, drop=True)

data_sorted = data_sorted.reset_index()
data_sorted.rename(columns={0:'consecutive_trending_days'}, inplace=True)
data_sorted

data_sorted.sort_values('consecutive_trending_days', ascending=False)

data_cons = pd.merge(data_first, data_sorted, on='video_id')

data_cons['consecutive_trending_days'].describe().to_frame()

plt.figure(figsize=(8,6))
data_cons['consecutive_trending_days'].plot(kind='hist')
plt.xlabel('Consecutive Trending Days', fontsize=15)
plt.ylabel('Freq', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Distribution of Consecutive Trending Days', fontsize=15)
plt.show()

data_cons.sort_values('consecutive_trending_days', ascending=False).head(10)

*

Number of Videos Trending based on Month of trending date¶

data['trending_month'] = data.trending_date.dt.month

plt.figure(figsize=(8,6))
sns.countplot(data = data, x = 'trending_month')
plt.xlabel('Month', fontsize=15)
plt.ylabel('Freq', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Number of videos by Months', fontsize=15)
plt.show()

print(data['trending_date'].min())
print(data['trending_date'].max())

2017-11-14 00:00:00
2018-06-14 00:00:00

data['publish_month'] = data.publish_time.dt.month

plt.figure(figsize=(8,6))
sns.countplot(data = data, x = 'publish_month')
plt.xlabel('Month', fontsize=15)
plt.ylabel('Freq', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Number of videos by Months', fontsize=15)
plt.show()

Segmentation¶

Topic Modeling on Title¶

nltk.download('averaged_perceptron_tagger')
df_title['pos_tags'] = df_title['title_tokenized'].apply(nltk.tag.pos_tag)

nltk.download('wordnet')
nltk.download('omw-1.4')
def get_wordnet_pos(tag):
    if tag.startswith('J'):
        return wordnet.ADJ
    elif tag.startswith('V'):
        return wordnet.VERB
    elif tag.startswith('N'):
        return wordnet.NOUN
    elif tag.startswith('R'):
        return wordnet.ADV
    else:
        return wordnet.NOUN
df_title['wordnet_pos'] = df_title['pos_tags'].apply(lambda x: [(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in x])

wnl = WordNetLemmatizer()
df_title['lemmatized'] = df_title['wordnet_pos'].apply(lambda x: [wnl.lemmatize(word, tag) for word, tag in x])

df_title['lemma_str'] = [' '.join(map(str,l)) for l in df_title['lemmatized']]
df_title.head()

[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!

tf_vectorizer = CountVectorizer(max_df=0.9, min_df=25, max_features=5000)
tf = tf_vectorizer.fit_transform(df_title['lemma_str'].values.astype('U'))
tf_feature_names = tf_vectorizer.get_feature_names()
doc_term_matrix = pd.DataFrame(tf.toarray(), columns=list(tf_feature_names))
doc_term_matrix

/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)

lda_model = LatentDirichletAllocation(n_components=30, learning_method='online', max_iter=20, random_state=0).fit(tf)
no_top_words = 10
def display_topics(model, feature_names, no_top_words):
    for topic_idx, topic in enumerate(model.components_):
        print("Topic %d:" % (topic_idx), end=' ')
        print(" ".join([feature_names[i]
                          for i in topic.argsort()[:-no_top_words - 1:-1]]))
              
display_topics(lda_model, tf_feature_names, no_top_words)

Topic 0: new movie 10 year bad team old star man find
Topic 1: go christmas every day first new time year ever never
Topic 2: game black play man come say woman golden panther hair
Topic 3: world test big perfect minute bbc film fall first christmas
Topic 4: video official ft try lyric music part explain tutorial family
Topic 5: star war top house nfl tour episode apple miss avenger
Topic 6: make best diy food battle interview work kevin cut stop
Topic 7: react use song mv iphone google hot make bts live
Topic 8: 2017 full know youtube great tb face like react use
Topic 9: get makeup week name friend car ad bts star perfect
Topic 10: live news watch stephen smith call president moment harry bowl
Topic 11: look night inside red dead high film lip first bbc
Topic 12: like scene secret people behind hit final show get refinery29
Topic 13: take last kid jedi school dance camila secret show star
Topic 14: love cake baby hot kim announcement grace name vs know
Topic 15: voice fire date justin lose cast wire kim national iphone
Topic 16: first time super bowl meet nba everything story girl love
Topic 17: one life thing buy march fox ad second vs bbc
Topic 18: ever chris national find never get hot christmas grace weird
Topic 19: vs real girl question vogue bts tv wire tom answer
Topic 20: show dog open snl white back hart youtube nbc fall
Topic 21: award american music win nbc fall let performance 2017 kim
Topic 22: trailer official hd talk season netflix teaser fan honest cook
Topic 23: espn john break giant shoot theory eat go netflix kevin
Topic 24: challenge highlight review box league grace adam audition food makeup
Topic 25: audio beauty feat official see cardi cover teaser make live
Topic 26: cat commercial car sport ice mom lip bowl challenge get
Topic 27: james face refinery29 holiday michael lebron olympics fall hot fire
Topic 28: 2018 trump wild give special jimmy need want nbc trailer
Topic 29: day home ball car minute use movie dog special let

pyLDAvis.enable_notebook()
panel = pyLDAvis.sklearn.prepare(lda_model, tf, tf_vectorizer, mds='tsne')
panel

/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  by='saliency', ascending=False).head(R).drop('saliency', 1)
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:827: FutureWarning: 'square_distances' has been introduced in 0.24 to help phase out legacy squaring behavior. The 'legacy' setting will be removed in 1.1 (renaming of 0.26), and the default setting will be changed to True. In 1.3, 'square_distances' will be removed altogether, and distances will be squared by default. Set 'square_distances'=True to silence this warning.
  FutureWarning,

lda_topic_values = lda_model.transform(tf)
data_first['lda_title'] = lda_topic_values.argmax(axis=1)

cnt_lda_title = data_first['lda_title'].value_counts().to_frame().reset_index()
cnt_lda_title.rename(columns = {'index':'Topic', 'lda_title':'Freq'}, inplace=True)
cnt_lda_title.Topic = cnt_lda_title.Topic.astype('str')
plt.figure(figsize=(20,10))
sns.barplot(data = cnt_lda_title.sort_values('Freq', ascending = False), x = 'Freq', y = 'Topic')
plt.title("LDA Topic Distribution on Title", fontsize=20)
plt.ylabel('Review Topics', fontsize=20)
plt.yticks(fontsize=20)
plt.xlabel('Frequency', fontsize=20)
plt.xticks(fontsize=20)

Topic Modeling on Tags¶

df_tags['pos_tags'] = df_tags['tags_tokenized'].apply(nltk.tag.pos_tag)

df_tags['wordnet_pos'] = df_tags['pos_tags'].apply(lambda x: [(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in x])

wnl = WordNetLemmatizer()
df_tags['lemmatized'] = df_tags['wordnet_pos'].apply(lambda x: [wnl.lemmatize(word, tag) for word, tag in x])

df_tags['lemma_str'] = [' '.join(map(str,l)) for l in df_tags['lemmatized']]
df_tags.head()

tf_vectorizer = CountVectorizer(max_df=0.9, min_df=25, max_features=5000)
tf = tf_vectorizer.fit_transform(df_tags['lemma_str'].values.astype('U'))
tf_feature_names = tf_vectorizer.get_feature_names()
doc_term_matrix = pd.DataFrame(tf.toarray(), columns=list(tf_feature_names))
doc_term_matrix

/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)

lda_model = LatentDirichletAllocation(n_components=30, learning_method='online', max_iter=20, random_state=0).fit(tf)
no_top_words = 10
              
display_topics(lda_model, tf_feature_names, no_top_words)

Topic 0: school high hack ever standup honest learn blue tesla amaze
Topic 1: animal take big king jack documentary great wild fry jr
Topic 2: espn perfect go charlie health bbc nick ted heart way
Topic 3: challenge react kid google liza reaction nfl brother fine know
Topic 4: make diy disney fox year box nintendo homemade world hannah
Topic 5: news smith nba trump basketball game animation live lebron break
Topic 6: interview sport celebrity none football entertainment game tonight magazine soccer
Topic 7: tv television car netflix series original john watch movie online
Topic 8: test voice buzzfeed food team try people taste best eat
Topic 9: 2017 live christmas award red 2018 holiday seth best meyers
Topic 10: makeup tutorial beauty black get hair face pet random video
Topic 11: family girl noggin funny anthony friendly fun boy robot lol
Topic 12: food recipe cook cake water kitchen hot bake world chef
Topic 13: star war last jedi bts cardi mark scene johnson explain
Topic 14: super bowl commercial chocolate green ice ad pizza money power
Topic 15: ellen dog man marvel talent show date fan degeneres panther
Topic 16: movie trailer 2018 film official theory new royal american ryan
Topic 17: life vlog house day home tour alex real fire prank
Topic 18: video cat show nbc today funny amazon kitten cute race
Topic 19: first time olympics woman ball winter 2018 meet evans korea
Topic 20: youtube channel simon education grace steve party audition press reveal
Topic 21: new science one york vs space city video adam travel
Topic 22: fashion style room trick trend secret every work fall like
Topic 23: night live snl sketch chris comedy season episode taylor host
Topic 24: game iphone review apple top 10 art design tech best
Topic 25: baby kardashian kim link good rhett news morning parody mythical
Topic 26: show late funny comedy talk stephen video joke humor celebrity
Topic 27: music video song pop record official play new love lyric
Topic 28: highlight fallon james graham lip full kelly battle bad sing
Topic 29: dance thing wwe kevin street la shot wrestle camera lilly

pyLDAvis.enable_notebook()
panel = pyLDAvis.sklearn.prepare(lda_model, tf, tf_vectorizer, mds='tsne')
panel

/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  by='saliency', ascending=False).head(R).drop('saliency', 1)
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:827: FutureWarning: 'square_distances' has been introduced in 0.24 to help phase out legacy squaring behavior. The 'legacy' setting will be removed in 1.1 (renaming of 0.26), and the default setting will be changed to True. In 1.3, 'square_distances' will be removed altogether, and distances will be squared by default. Set 'square_distances'=True to silence this warning.
  FutureWarning,

lda_topic_values = lda_model.transform(tf)
data_first['lda_tags'] = lda_topic_values.argmax(axis=1)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

cnt_lda_tags = data_first['lda_tags'].value_counts().to_frame().reset_index()
cnt_lda_tags.rename(columns = {'index':'Topic', 'lda_tags':'Freq'}, inplace=True)
cnt_lda_tags.Topic = cnt_lda_tags.Topic.astype('str')
plt.figure(figsize=(20,10))
sns.barplot(data = cnt_lda_tags.sort_values('Freq', ascending = False), x = 'Freq', y = 'Topic')
plt.title("LDA Topic Distribution on Tags", fontsize=20)
plt.ylabel('Review Topics', fontsize=20)
plt.yticks(fontsize=20)
plt.xlabel('Frequency', fontsize=20)
plt.xticks(fontsize=20)

(array([  0., 100., 200., 300., 400., 500., 600.]),
 <a list of 7 Text major ticklabel objects>)

data_first.head()

# %%shell
# jupyter nbconvert --to html //Your notebook path file.ipynb

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1	1ZAPwfrtAFY	17.14.11	The Trump Presidency: Last Week Tonight with J...	LastWeekTonight	24	2017-11-13T07:30:00.000Z	last week tonight trump presidency\|"last week ...	2418783	97185	6146	12703	https://i.ytimg.com/vi/1ZAPwfrtAFY/default.jpg	False	False	False	One year after the presidential election, John...
2	5qpjK5DgCt4	17.14.11	Racist Superman \| Rudy Mancuso, King Bach & Le...	Rudy Mancuso	23	2017-11-12T19:05:24.000Z	racist superman\|"rudy"\|"mancuso"\|"king"\|"bach"...	3191434	146033	5339	8181	https://i.ytimg.com/vi/5qpjK5DgCt4/default.jpg	False	False	False	WATCH MY PREVIOUS VIDEO ▶ \n\nSUBSCRIBE ► http...
3	puqaWrEC7tY	17.14.11	Nickelback Lyrics: Real or Fake?	Good Mythical Morning	24	2017-11-13T11:00:04.000Z	rhett and link\|"gmm"\|"good mythical morning"\|"...	343168	10172	666	2146	https://i.ytimg.com/vi/puqaWrEC7tY/default.jpg	False	False	False	Today we find out if Link is a Nickelback amat...
4	d380meD0W0M	17.14.11	I Dare You: GOING BALD!?	nigahiga	24	2017-11-12T18:01:41.000Z	ryan\|"higa"\|"higatv"\|"nigahiga"\|"i dare you"\|"...	2095731	132235	1989	17518	https://i.ytimg.com/vi/d380meD0W0M/default.jpg	False	False	False	I know it's been a while since we did this sho...

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1	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/UZ1oLIIz2dxIhO45Z...	2	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
2	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/nqRIq97-xe5XRZTxb...	10	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
3	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/HwXKamM1Q20q9BN-o...	15	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
4	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/9GQMSRjrZdHeb1OEM...	17	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
5	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/FJwVpGCVZ1yiJrqZb...	18	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
6	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/M-3iD9dwK7YJCafRf...	19	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
7	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/WmA0qYEfjWsAoyJFS...	20	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
8	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/EapFaGYG7K0StIXVf...	21	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
9	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/xId8RX7vRN8rqkbYZ...	22	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
10	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/G9LHzQmx44rX2S5ya...	23	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
11	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/UVB9oxX2Bvqa_w_y3...	24	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
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13	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/r6Ck6Z0_L0rG37VJQ...	26	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
14	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/EoYkczo9I3RCf96Rv...	27	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
15	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/w5HjcTD82G_XA3xBc...	28	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
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17	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/lL7uWDr_071CHxifj...	30	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
18	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/WnuVfjO-PyFLO7NTR...	31	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
19	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/ctpH2hGA_UZ3volJT...	32	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
20	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/L0kR3-g1BAo5UD1PL...	33	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
21	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/pUZOAC_s9sfiwar63...	34	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
22	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/Xb5JLhtyNRN3AQq02...	35	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
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24	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/D04PP4Gr7wc4IV_O9...	37	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
25	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/i5-_AceGXQCEEMWU0...	38	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
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27	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/N1TrDFLRppxZgBowC...	40	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
28	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/7UMGi6zRySqXopr_r...	41	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
29	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/RScXhi324h8usyIet...	42	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
30	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/0n9MJVCDLpA8q7aiG...	43	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
31	youtube#videoCategory	"m2yskBQFythfE4irbTIeOgYYfBU/x5NxSf5fz8hn4loSN...	44	{'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...

	video_id	trending_date	title	channel_title	category_id	publish_time	tags	views	likes	dislikes	comment_count	comments_disabled	ratings_disabled	video_error_or_removed	description	id	category
0	2kyS6SvSYSE	17.14.11	WE WANT TO TALK ABOUT OUR MARRIAGE	CaseyNeistat	22	2017-11-13T17:13:01.000Z	SHANtell martin	748374	57527	2966	15954	False	False	False	SHANTELL'S CHANNEL - https://www.youtube.com/s...	22	People & Blogs
1	0mlNzVSJrT0	17.14.11	Me-O Cats Commercial	Nobrand	22	2017-04-21T06:47:32.000Z	cute\|"cats"\|"thai"\|"eggs"	98966	2486	184	532	False	False	False	Kittens come out of the eggs in a Thai commerc...	22	People & Blogs
2	STI2fI7sKMo	17.14.11	AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -...	Shawn Johnson East	22	2017-11-11T15:00:03.000Z	shawn johnson\|"andrew east"\|"shawn east"\|"shaw...	321053	4451	1772	895	False	False	False	Subscribe for weekly videos ▶ http://bit.ly/sj...	22	People & Blogs
3	KODzih-pYlU	17.14.11	BLIND(folded) CAKE DECORATING CONTEST (with Mo...	Grace Helbig	22	2017-11-11T18:08:04.000Z	itsgrace\|"funny"\|"comedy"\|"vlog"\|"grace"\|"helb...	197062	7250	217	456	False	False	False	Molly is an god damn amazing human and she cha...	22	People & Blogs
4	8mhTWqWlQzU	17.14.11	Wearing Online Dollar Store Makeup For A Week	Safiya Nygaard	22	2017-11-11T01:19:33.000Z	wearing online dollar store makeup for a week\|...	2744430	115426	1110	6541	False	False	False	I found this online dollar store called ShopMi...	22	People & Blogs

	video_id	title	channel_title	tags	description	category
0	2kyS6SvSYSE	WE WANT TO TALK ABOUT OUR MARRIAGE	CaseyNeistat	SHANtell martin	SHANTELL'S CHANNEL - https://www.youtube.com/s...	People & Blogs
1	0mlNzVSJrT0	Me-O Cats Commercial	Nobrand	cute\|"cats"\|"thai"\|"eggs"	Kittens come out of the eggs in a Thai commerc...	People & Blogs
2	STI2fI7sKMo	AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -...	Shawn Johnson East	shawn johnson\|"andrew east"\|"shawn east"\|"shaw...	Subscribe for weekly videos ▶ http://bit.ly/sj...	People & Blogs
3	KODzih-pYlU	BLIND(folded) CAKE DECORATING CONTEST (with Mo...	Grace Helbig	itsgrace\|"funny"\|"comedy"\|"vlog"\|"grace"\|"helb...	Molly is an god damn amazing human and she cha...	People & Blogs
4	8mhTWqWlQzU	Wearing Online Dollar Store Makeup For A Week	Safiya Nygaard	wearing online dollar store makeup for a week\|...	I found this online dollar store called ShopMi...	People & Blogs
...	...	...	...	...	...	...
40883	pwGbwYAfSmg	5 books worth reading this summer	Bill Gates	Bill\|"Gates"	If you’re looking for something to read over t...	Nonprofits & Activism
40892	lM0yu7c6lQk	You're not crazy. Apple is slowing down older ...	CNET	Apple\|"iPhone Slowdown"\|"Apple Battery"\|"Cultu...	Apple comes clean after overwhelming evidence ...	Shows
40903	Q1CFfU2gXHw	Apple HomePod: Everything to know before you b...	CNET	Apple\|"Homepod"\|"smart home"\|"Google Home"\|"Am...	We'll break down all the new Apple HomePod det...	Shows
40908	7_FJUSBFbJM	Game of Zones - Game of Zones - S5:E1: 'A Gold...	Bleacher Report	bleacher report\|"br"\|"nba"\|"game of zones"\|"go...	In the season 5 premiere of Game of Zones, the...	Shows
40924	V6ElE2xs48c	Game of Zones - S5:E5: The Isle of Van Gundy	Bleacher Report	bleacher report\|"br"\|"nba"\|"Stan Van Gundy"\|"J...	On an island full of Van Gundys, Stan gets tal...	Shows

	title_cleaned	title_tokenized
0	want talk marriage	[want, talk, marriage]
1	meo cats commercial	[meo, cats, commercial]
2	affairs ex boyfriends 18million net worth goog...	[affairs, ex, boyfriends, 18million, net, wort...
3	blindfolded cake decorating contest molly burk...	[blindfolded, cake, decorating, contest, molly...
4	wearing online dollar store makeup week	[wearing, online, dollar, store, makeup, week]

	desc_cleaned	desc_tokenized
0	shantells channel https www youtube com shante...	[shantells, channel, https, www, youtube, com,...
1	kittens come eggs thai commercialnnreuploaded ...	[kittens, come, eggs, thai, commercialnnreuplo...
2	subscribe weekly videos http bit ly sjofficoak...	[subscribe, weekly, videos, http, bit, ly, sjo...
3	molly god damn amazing human challenged blind ...	[molly, god, damn, amazing, human, challenged,...
4	found online dollar store called shopmissa sel...	[found, online, dollar, store, called, shopmis...

	tags_cleaned	tags_tokenized
0	shantell martin	[shantell, martin]
1	cute cats thai eggs	[cute, cats, thai, eggs]
2	shawn johnson andrew east shawn east shawn and...	[shawn, johnson, andrew, east, shawn, east, sh...
3	itsgrace funny comedy vlog grace helbig graceh...	[itsgrace, funny, comedy, vlog, grace, helbig,...
4	wearing online dollar store makeup week online...	[wearing, online, dollar, store, makeup, week,...

	title	channel_title	category	publish_time	comment_count
5912	So Sorry.	Logan Paul Vlogs	Entertainment	2018-01-02 16:42:21+00:00	1361580
31839	BTS (방탄소년단) 'FAKE LOVE' Official MV	ibighit	Music	2018-05-18 09:00:02+00:00	1228655
4703	YouTube Rewind: The Shape of 2017 \| #YouTubeRe...	YouTube Spotlight	Entertainment	2017-12-06 17:58:51+00:00	810698
40865	Suicide: Be Here Tomorrow.	Logan Paul Vlogs	Nonprofits & Activism	2018-01-24 18:30:01+00:00	658130
26673	BTS (방탄소년단) 'MIC Drop (Steve Aoki Remix)' Offi...	ibighit	Music	2017-11-24 09:00:02+00:00	546100

	title	channel_title	category	publish_time	views
31902	Childish Gambino - This Is America (Official V...	ChildishGambinoVEVO	Music	2018-05-06 04:00:07+00:00	225211923
4703	YouTube Rewind: The Shape of 2017 \| #YouTubeRe...	YouTube Spotlight	Entertainment	2017-12-06 17:58:51+00:00	149376127
31170	Ariana Grande - No Tears Left To Cry	ArianaGrandeVevo	Music	2018-04-20 04:00:03+00:00	148689896
31207	Becky G, Natti Natasha - Sin Pijama (Official ...	BeckyGVEVO	Music	2018-04-20 10:40:51+00:00	139334502
31839	BTS (방탄소년단) 'FAKE LOVE' Official MV	ibighit	Music	2018-05-18 09:00:02+00:00	123010920

	video_id	trending_date	title	channel_title	category_id	publish_time	tags	views	likes	dislikes	comment_count	comments_disabled	ratings_disabled	video_error_or_removed	description	id	category	upload_to_trending
0	2kyS6SvSYSE	2017-11-14	WE WANT TO TALK ABOUT OUR MARRIAGE	CaseyNeistat	22	2017-11-13 17:13:01+00:00	SHANtell martin	748374	57527	2966	15954	False	False	False	SHANTELL'S CHANNEL - https://www.youtube.com/s...	22	People & Blogs	1
1	0mlNzVSJrT0	2017-11-14	Me-O Cats Commercial	Nobrand	22	2017-04-21 06:47:32+00:00	cute\|"cats"\|"thai"\|"eggs"	98966	2486	184	532	False	False	False	Kittens come out of the eggs in a Thai commerc...	22	People & Blogs	207
2	STI2fI7sKMo	2017-11-14	AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -...	Shawn Johnson East	22	2017-11-11 15:00:03+00:00	shawn johnson\|"andrew east"\|"shawn east"\|"shaw...	321053	4451	1772	895	False	False	False	Subscribe for weekly videos ▶ http://bit.ly/sj...	22	People & Blogs	3
3	KODzih-pYlU	2017-11-14	BLIND(folded) CAKE DECORATING CONTEST (with Mo...	Grace Helbig	22	2017-11-11 18:08:04+00:00	itsgrace\|"funny"\|"comedy"\|"vlog"\|"grace"\|"helb...	197062	7250	217	456	False	False	False	Molly is an god damn amazing human and she cha...	22	People & Blogs	3
4	8mhTWqWlQzU	2017-11-14	Wearing Online Dollar Store Makeup For A Week	Safiya Nygaard	22	2017-11-11 01:19:33+00:00	wearing online dollar store makeup for a week\|...	2744430	115426	1110	6541	False	False	False	I found this online dollar store called ShopMi...	22	People & Blogs	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
40883	pwGbwYAfSmg	2018-05-27	5 books worth reading this summer	Bill Gates	29	2018-05-21 13:30:33+00:00	Bill\|"Gates"	250175	8455	126	552	False	False	False	If you’re looking for something to read over t...	29	Nonprofits & Activism	6
40892	lM0yu7c6lQk	2017-12-23	You're not crazy. Apple is slowing down older ...	CNET	43	2017-12-22 01:16:16+00:00	Apple\|"iPhone Slowdown"\|"Apple Battery"\|"Cultu...	110112	3547	180	1426	False	False	False	Apple comes clean after overwhelming evidence ...	43	Shows	1
40903	Q1CFfU2gXHw	2018-01-26	Apple HomePod: Everything to know before you b...	CNET	43	2018-01-24 20:08:16+00:00	Apple\|"Homepod"\|"smart home"\|"Google Home"\|"Am...	36609	810	66	220	False	False	False	We'll break down all the new Apple HomePod det...	43	Shows	2
40908	7_FJUSBFbJM	2018-04-14	Game of Zones - Game of Zones - S5:E1: 'A Gold...	Bleacher Report	43	2018-04-12 21:00:12+00:00	bleacher report\|"br"\|"nba"\|"game of zones"\|"go...	657183	24107	310	1825	False	False	False	In the season 5 premiere of Game of Zones, the...	43	Shows	2
40924	V6ElE2xs48c	2018-05-12	Game of Zones - S5:E5: The Isle of Van Gundy	Bleacher Report	43	2018-05-10 21:01:22+00:00	bleacher report\|"br"\|"nba"\|"Stan Van Gundy"\|"J...	765531	18681	461	1444	False	False	False	On an island full of Van Gundys, Stan gets tal...	43	Shows	2

	upload_to_trending
count	6351.000000
mean	22.522280
std	209.963296
min	0.000000
25%	1.000000
50%	2.000000
75%	3.000000
max	4215.000000

	video_id	trending_date	title	channel_title	category_id	publish_time	tags	views	likes	dislikes	comment_count	comments_disabled	ratings_disabled	video_error_or_removed	description	id	category	upload_to_trending
7054	MJO3FmmFuh4	2018-02-05	Budweiser - Original Whazzup? ad	dannotv	24	2006-07-23 08:24:11+00:00	Budweiser\|"Bud"\|"Whazzup"\|"ad"	258506	459	152	82	False	False	False	Original Whazzup ad - however, there is a litt...	24	Entertainment	4215
19639	UQtt9I6c-YM	2018-01-06	Kramer vs Kramer-Clou Scene	Livia Giustiniani	1	2008-04-05 18:22:40+00:00	Meryl\|"Streep"\|"kramer"\|"vs"\|"dustin"\|"hoffman...	49942	46	6	26	False	False	False	Poor Meryl...she was really scaredxD	1	Film & Animation	3563
26483	wFEAoF7RC7Y	2017-11-25	Behind The Sounds: That's Not Me	BehindTheSounds	10	2008-06-17 00:07:56+00:00	Beach\|"Boys"\|"Brian"\|"Wilson"\|"Dennis"\|"Carl"\|...	86744	469	4	105	False	False	False	A video detailing the creative process behind ...	10	Music	3448
26522	Y8JGfi4MJ8k	2017-11-26	SAOIRSE RONAN - MORONIC (IRONIC)	flirppy242	10	2008-08-07 12:16:58+00:00	SAOIRSE\|"RONAN"\|"MORONIC"\|"IRONIC"\|"IZZIE"\|"CO...	26482	78	1	7	False	False	False	EL NOVIO DE MI MADRE\r\nI COULD NEVER BE A WOMAN	10	Music	3398
19087	rO_mAQC9bv0	2017-11-19	Amazon.com commercial Xmas 1999Magnetic Whale Art	astroboy1960	1	2009-03-10 00:27:20+00:00	amazon\|"commerical"\|"y2k"\|"sing"\|"along"\|"mitc...	15382	33	1	10	False	False	False	IMHO the best of Amazon's Christmas 1999 ads. ...	1	Film & Animation	3176

	video_id	consecutive_trending_days
0	-0CMnp02rNY	6
1	-0NYY8cqdiQ	1
2	-1Hm41N0dUs	3
3	-1yT-K3c6YI	4
4	-2RVw2_QyxQ	3
...	...	...
6954	zxUwbflE1SY	5
6955	zxwfDlhJIpw	13
6956	zxwfDlhJIpw	10
6957	zy0b9e40tK8	1
6958	zzQsGL_F9_c	2

	video_id	consecutive_trending_days
6257	t4pRQ0jn23Q	28
811	6S9c5nnDd_s	28
2978	QBL8IRJ5yHU	28
2526	MAjY8mCTXWk	28
5987	r-3iathMo7o	28
...	...	...
6030	rL4WkebTT_U	1
6029	rJfFxDQ-8ok	1
3065	R9s5_Ji33xY	1
3070	RBpLVXNN6i0	1
3479	V1Pl8CzNzCw	1

	consecutive_trending_days
count	6959.000000
mean	5.884322
std	4.367000
min	1.000000
25%	3.000000
50%	5.000000
75%	8.000000
max	28.000000

	video_id	trending_date	title	channel_title	category_id	publish_time	tags	views	likes	dislikes	comment_count	comments_disabled	ratings_disabled	video_error_or_removed	description	id	category	upload_to_trending	consecutive_trending_days
6642	QBL8IRJ5yHU	2018-05-15	Why I'm So Scared (being myself and crying too...	grav3yardgirl	26	2018-05-14 19:00:01+00:00	beauty\|"how to"\|"makeup"\|"howto"\|"style"\|"fash...	1469627	188652	3124	33032	False	False	False	I will never be able to say Thank You enough.....	26	Howto & Style	1	28
3332	r-3iathMo7o	2018-05-15	The ULTIMATE $30,000 Gaming PC Setup	Unbox Therapy	28	2018-05-13 19:00:25+00:00	gaming setup\|"gaming"\|"setup"\|"fortnite"\|"fort...	4700460	103430	8028	13293	False	False	False	Fortnite, PUBG, Far Cry 5? Which game would yo...	28	Science & Technology	2	28
2183	t4pRQ0jn23Q	2018-05-15	YoungBoy Never Broke Again Goes Sneaker Shoppi...	Complex	24	2018-05-14 14:00:03+00:00	sneakerhead\|"complex"\|"complex originals"\|"sne...	1199587	49709	2380	7261	False	False	False	YoungBoy Never Broke Again goes Sneaker Shoppi...	24	Entertainment	1	28
2175	j4KvrAUjn6c	2018-05-14	WE MADE OUR MOM CRY...HER DREAM CAME TRUE!	Lucas and Marcus	24	2018-05-13 18:03:56+00:00	Lucas and Marcus\|"Marcus and Lucas"\|"Dobre"\|"D...	2131702	54280	4696	10705	False	False	False	BEST MOM EVER! TIX ON SALE NOW!! https://www.d...	24	Entertainment	1	28
3670	6S9c5nnDd_s	2018-05-16	Bohemian Rhapsody \| Teaser Trailer [HD] \| 20th...	20th Century Fox	1	2018-05-15 13:01:12+00:00	Trailer\|"bohemian rhapsody"\|"queen"\|"freddie m...	4215671	161058	2198	10289	False	False	False	Visit http://www.PutMeInBohemian.com for a cha...	1	Film & Animation	1	28
3669	NBSAQenU2Bk	2018-05-15	Rooster Teeth Animated Adventures - Millie So ...	Rooster Teeth	1	2018-05-14 13:00:01+00:00	Rooster Teeth\|"RT"\|"animation"\|"television"\|"f...	404162	17920	79	642	False	False	False	Millie is invited to help out at a Sugar Pine ...	1	Film & Animation	1	28
5556	MAjY8mCTXWk	2018-05-15	周杰倫 Jay Chou【不愛我就拉倒 If You Don't Love Me, It's...	杰威爾音樂 JVR Music	10	2018-05-14 15:59:47+00:00	周杰倫\|"Jay"\|"Chou"\|"周董"\|"周杰伦"\|"周傑倫"\|"杰威尔"\|"周周"\|"...	916128	40485	1042	4746	False	False	False	詞：周杰倫、宋健彰（彈頭）曲：周杰倫憂鬱型男的走心旋律用英式搖滾宣洩情傷不愛我就拉倒...	10	Music	1	28
5565	H7gh2fmdjCU	2018-05-18	Calum Scott - What I Miss Most	CalumScottVEVO	10	2018-05-16 23:00:00+00:00	Calum\|"Scott"\|"What"\|"Miss"\|"Most"\|"Capitol"\|"...	664458	45750	289	2354	False	False	False	Calum’s debut album ‘Only Human’ feat. “You Ar...	10	Music	2	27
2894	1purAy2MsOc	2018-05-16	22 Years of Life Milestone	TheOdd2sOut	23	2018-05-14 19:36:05+00:00	Birthday\|"rice"\|"cake"\|"one million"\|"Milestone"	688645	73735	466	23002	False	False	False	Thanks for an amazing year!(Im pretty behind o...	23	Comedy	2	27
5564	6SuMbFuKDf8	2018-05-18	Backstreet Boys - Don't Go Breaking My Heart (...	BackstreetBoysVEVO	10	2018-05-17 04:00:01+00:00	Backstreet Boys\|"Don't Go Breaking My Heart"\|"...	2822391	131909	5164	16487	False	False	False	Get the Backstreet Boys new single “Don’t Go B...	10	Music	1	27

	title_cleaned	title_tokenized	pos_tags	wordnet_pos	lemmatized	lemma_str
0	want talk marriage	[want, talk, marriage]	[(want, JJ), (talk, NN), (marriage, NN)]	[(want, a), (talk, n), (marriage, n)]	[want, talk, marriage]	want talk marriage
1	meo cats commercial	[meo, cats, commercial]	[(meo, NN), (cats, NNS), (commercial, JJ)]	[(meo, n), (cats, n), (commercial, a)]	[meo, cat, commercial]	meo cat commercial
2	affairs ex boyfriends 18million net worth goog...	[affairs, ex, boyfriends, 18million, net, wort...	[(affairs, NNS), (ex, VBD), (boyfriends, NNS),...	[(affairs, n), (ex, v), (boyfriends, n), (18mi...	[affair, ex, boyfriend, 18million, net, worth,...	affair ex boyfriend 18million net worth google...
3	blindfolded cake decorating contest molly burk...	[blindfolded, cake, decorating, contest, molly...	[(blindfolded, VBN), (cake, NN), (decorating, ...	[(blindfolded, v), (cake, n), (decorating, v),...	[blindfold, cake, decorate, contest, molly, bu...	blindfold cake decorate contest molly burke gr...
4	wearing online dollar store makeup week	[wearing, online, dollar, store, makeup, week]	[(wearing, VBG), (online, JJ), (dollar, NN), (...	[(wearing, v), (online, a), (dollar, n), (stor...	[wear, online, dollar, store, makeup, week]	wear online dollar store makeup week

	10	2017	2018	ad	adam	american	announcement	answer	apple	audio	...	weird	white	wild	win	wire	woman	work	world	year	youtube
0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6346	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6347	0	0	0	0	0	0	0	0	2	0	...	0	0	0	0	0	0	0	0	0	0
6348	0	0	0	0	0	0	0	0	2	0	...	0	0	0	0	0	0	0	0	0	0
6349	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6350	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	tags_cleaned	tags_tokenized	pos_tags	wordnet_pos	lemmatized	lemma_str
0	shantell martin	[shantell, martin]	[(shantell, NN), (martin, NN)]	[(shantell, n), (martin, n)]	[shantell, martin]	shantell martin
1	cute cats thai eggs	[cute, cats, thai, eggs]	[(cute, NN), (cats, NNS), (thai, VBP), (eggs, ...	[(cute, n), (cats, n), (thai, v), (eggs, n)]	[cute, cat, thai, egg]	cute cat thai egg
2	shawn johnson andrew east shawn east shawn and...	[shawn, johnson, andrew, east, shawn, east, sh...	[(shawn, NN), (johnson, NN), (andrew, VBD), (e...	[(shawn, n), (johnson, n), (andrew, v), (east,...	[shawn, johnson, andrew, east, shawn, east, sh...	shawn johnson andrew east shawn east shawn and...
3	itsgrace funny comedy vlog grace helbig graceh...	[itsgrace, funny, comedy, vlog, grace, helbig,...	[(itsgrace, NN), (funny, JJ), (comedy, NN), (v...	[(itsgrace, n), (funny, a), (comedy, n), (vlog...	[itsgrace, funny, comedy, vlog, grace, helbig,...	itsgrace funny comedy vlog grace helbig graceh...
4	wearing online dollar store makeup week online...	[wearing, online, dollar, store, makeup, week,...	[(wearing, VBG), (online, JJ), (dollar, NN), (...	[(wearing, v), (online, a), (dollar, n), (stor...	[wear, online, dollar, store, makeup, week, on...	wear online dollar store makeup week online do...